Field of the Invention
This application relates to methods for evaluating tissue image analysis feature distribution functions with the intent to stratify patient cohorts into two or more distinct categories of interest. More specifically, the method utilizes digital tissue image analysis to extract staining and morphometric features from images of stained tissue sections, quantifies the distribution of one or more image analysis feature, and applies a patient selection paradigm based on a comparison of the patient-specific image analysis feature distribution to a reference distribution or value to identify patients as candidates for a specific therapy.
Description of the Related Art
The majority of current in vitro diagnostic assays, laboratory developed tests, and research use only assays are based on measuring the staining intensity of biomarkers of interest, such as HER2. The biomarker is visualized by using antibodies, histologic dyes, or in situ hybridization probes to detect the biomarker of interest and detection reagents such as chromogenic and fluorescent stains or dyes. Typically, evaluation of the staining, as a surrogate for the biomarker, is assessed manually by a pathologist. In some instances, digital image analysis algorithms which are configured to mimic manual scoring paradigms can also be used to evaluate biomarker expression in tissue. In general, these approaches often condense, in an over simplified manner, the sometimes heterogeneous distribution of staining intensities into a single summary score for a tissue.
As an illustrative example, the H-Score paradigm consists of a pathologist assessing two different conditions in a given tissue to assign a score. The pathologist categorizes staining on a semi-quantitative scale of 0, 1, 2, and 3+(negative, low, medium, and high expression, respectively). In addition, the pathologist must assign the percentage of tissue or cells within the tissue that falls into each category, with all four categories adding to 100%. Finally, an H-score is calculated by multiplying the score category (i.e. 0, 1, 2, 3+) by the percentage of tissue (i.e. 0-100%) in that category and results in a score ranging from 0 to 300.
Interestingly, with the H-Score paradigm, two different tissues with unique staining distributions (e.g. one tissue with 100% of cells staining in the 1+ category and a second tissue with 50% of cells staining in the 0 and the other 50% of cells staining in the 2+ categories) can have the same H-score (e.g. an H-Score of 100 for both tissues), and illustrates the conundrum with present scoring paradigms which overly simplify complex staining information into a single summary score.
Alternatively, a tissue can be scored, as a whole, on a 0, 1, 2, and 3+ scale. This scoring approach assigns a score to a tissue based on the maximum staining level observed in a specified percentage of the tissue (e.g. strong staining in >15% of tumor cells scores a tissue sample as 3+). In this scoring paradigm, only the staining intensity of a subset of cells or tissue area is considered, and the information contained in the remainder of the tissue is discarded in the summary score.
While these scoring approaches have demonstrated utility for evaluating biomarkers, there are instances where the scoring paradigms are insufficient to capture the necessary granularity of biomarker expression to guide insights or patient selection decisions. Digital tissue image analysis has evolved into a powerful tool for extracting a great deal of information from stained tissue. Digital image analysis can quantify many features, both morphometric and staining, related to biomarker and tissue presentation for use in developing novel scoring paradigms.